FIN2119: Financial Decision Support Tools

Assignment Overview

Task Description

This assessment gives students the opportunity to analyse and apply forecasting tools to financial data in order to support improved decision-making. You will be required to work with the provided case study and demonstrate your understanding of forecasting models and their practical application in a business context.

Task Details

Students must answer the questions related to the case study outlined below. You are required to present a report of no more than 1,000 words addressed to the company manager, explaining your results and the rationale behind the forecasts.
As an alternative, you may present your findings as a PowerPoint presentation with no more than 20 slides.

The structure of the report is flexible and may include any information you consider valuable. However, the manager has requested specific questions to be addressed, and certain points must be discussed as outlined in the assessment questions.

Forecasting Requirements

To complete this assessment, you are required to develop two forecasting models, using the provided case study data:

1. A multiplicative time series decomposition forecasting model

2. An exponentially smoothed model using a smoothing coefficient (weight) of 0.4

You must use Microsoft Excel to generate both forecasting models. The relevant Excel output should be included in your submission to support your analysis and conclusions.

Summary of Assessment Requirements

The assessment required students to analyse financial data and apply forecasting tools to support managerial decision-making. Using the case study provided, students needed to:

Key Requirements

• Develop two forecasting models using Microsoft Excel:

  1. Multiplicative Time Series Decomposition model

  2. Exponential Smoothing model with a smoothing coefficient (α = 0.4)

• Present the results in either:

  • A written report (max. 1,000 words) addressed to the company manager, or

  • A PowerPoint presentation (max. 20 slides).

• Answer the specific questions outlined in the case study and explain:

  • The forecasting process

  • Interpretation of results

  • Rationale behind chosen forecasting approaches

  • Implications for business decision-making

• Include relevant Excel outputs (tables, charts, and model results).

How the Academic Mentor Guided the Student 

The academic mentor supported the student by breaking down the assessment into manageable, sequential tasks and ensuring the student understood both the technical modelling process and the reporting requirements.

Step 1: Understanding the Assessment Brief

The mentor first helped the student:

• Interpret the task description

• Identify the required forecasting tools

• Recognise the importance of decision support in business forecasting

• Clarify the manager’s expectations

This step ensured the student knew what to deliver and how Excel-based forecasting connects to managerial decision-making.

Step 2: Reviewing the Case Study Data

The mentor guided the student to:

• Inspect the historical financial data

• Identify seasonal patterns, trends, and cycles

• Organise the data correctly in Excel for analysis

This helped establish a foundation for both forecasting models.

Step 3: Building the Multiplicative Time Series Decomposition Model

The mentor provided step-wise direction on:

1. Plotting the original time series

2. Identifying trend, seasonality, and irregular components

3. Using Excel formulas and moving averages to derive:

   • Trend estimate

   • Seasonal indices

   • De-seasonalised data

4. Reconstructing the forecast using the multiplicative structure:

   Forecast = Trend × Seasonal Index

5. Formatting the output for reporting

The mentor ensured the student understood not only how to calculate, but why each step matters for analysing business data.

Step 4: Developing the Exponential Smoothing Model (α = 0.4)

The mentor guided the student to:

• Enter the smoothing formula in Excel

• Apply the given smoothing weight (0.4)

• Create a forecast column for each future period

• Compare smoothing results with decomposition outputs

This trained the student to evaluate model responsiveness and reliability.

Step 5: Interpreting the Forecasting Results

The mentor helped the student:

• Compare the performance of both models

• Identify trends, seasonal behaviour, and forecast accuracy

• Explain how the manager can use these results for planning

• Connect findings to operational and financial decisions

This enabled the student to transition from numerical results to meaningful business insights.

Step 6: Structuring the Final Report

The mentor guided the student in organising the final report with:

• A professional introduction

• Clear explanation of methodology

• Well-labelled Excel outputs

• Interpretation and implications

• A concise conclusion with recommendations

The mentor emphasised clarity, logical flow, and relevance to managerial decision-making.

Final Outcome & Learning Achievements

By following the structured mentoring process, the student successfully produced a concise and analytical report meeting all assessment requirements. The final outcome:

Delivered:

• A 1,000-word professional report addressed to the manager

• Two forecasting models (Decomposition and Exponential Smoothing)

• Correct Excel calculations and visual outputs

• Clear reasoning behind forecasts

• Management-oriented recommendations

Learning Objectives Achieved

The student demonstrated the ability to:

Analyse time-series financial data
Apply decomposition and exponential smoothing techniques
Use Excel effectively for forecasting
Interpret model results in a business context
Communicate technical findings to non-technical decision-makers
Make evidence-based recommendations

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